Like their brick and mortar counterparts, online businesses are always searching for new ways to retain their customers and turn them into repeat purchasers. This is because returning customers deliver a whopping 40% of all revenue, despite accounting for only 8% of visitors!
One easy - and free - way to ensure these valuable customers keep coming back is by calculating your store’s purchase frequency and using the resulting data to direct and shape your marketing campaigns and outreach efforts. With one simple metric, you can transform your business without squandering your time, effort, and budget.
Sounds good, right? Before we get ahead of ourselves, though, let’s start at the beginning and answer the most pressing question.
What is Purchase Frequency and Why Does it Matter?
While this is a topic we’ve written about before, it never hurts to review why and how we calculate important metrics. In the simplest terms, your purchase frequency refers to how often the average shopper makes a purchase at your store.
The importance of this metric is clear: bringing customers back to your store with greater frequency will, of course, increase your sales. Beyond that, though, an elevated purchase frequency increases both revenue and customer lifetime value - something every business stands to benefit from.
You can calculate your purchase frequency by dividing your store’s total number of orders by your number of unique customers. Once you know your purchase frequency, you can start analyzing the data to figure out whether your customers tend to complete their purchases at a certain time of year and when the most profitable purchases are made.
This data gives you a lot more context into what your customers are looking for - and when. As a result, you’ll be better equipped to create marketing campaigns that target your most profitable customers at the right time.
But more on that later. For now, let’s dig a little deeper into how we can quantify the value of purchase frequency and how it affects the probability of a customer shopping with you again.
Calculating Purchase Frequency (PF) with Repeat Purchase Probability (RPP)
In order to understand the effects of repeat purchase probability (RPP) on purchase frequency (PF), we’re going to illustrate some of the necessary calculations using a sample dataset for one year of sales from an online retailer.
As noted above, we can calculate the PF by summing up unique order counts per each customer. This way, we get a chart that looks like the one below:
As you can see, the number of orders (vertical axis) decreases pretty quickly after the first purchase is made (horizontal axis). To see this with more perspective, let’s take a look at the total revenue for each cohort of customers that bought a specific number of times in the given period. To calculate the revenue, we will use an average order value (AOV) of $40 and multiply it by the number of orders.
While it’s no surprise that the biggest amount of revenue is still driven by first time buyers, the story changes when we compare it to repeat purchases. When you put these two numbers side by side, you can see that it amounts to 41% of total revenue. This matches findings from Adobe’s research.
The interesting part, then, is how to calculate the probability of a first time buyers making a repeated purchase (RPP). Luckily our friends over at RJmetrics have provided us with a simple formula that does exactly that:
Let’s break this down even further. Suppose you had started a new business and had 130 customers: 10 who have made three purchases, 20 who have made two purchases, and 100 that have made only one purchase.
When you break RJ Metrics’ formula down, your customer data would look something like this:
With this in mind, we can plug in our metaphorical store data in order to calculate its repeat purchase probability:
This same formula can be modified to calculate the probability of a second and third purchase, and so on.
With this calculation in mind, let’s see how RPP stacks up against purchase frequency for the sample dataset we were working with earlier.
As you can see, repeat purchase probability almost doubles once a customer buys a second time, and keeps increasing through successive purchases up to a point. As a result, we can argue that the more frequently a customer buys, the more habitual it becomes, thereby increasing the probability of a next purchase.
Of course this information is not useful if we cannot quantify the value of trying to increase the probability of someone buying again. Fortunately, we can do it fairly easily by using the same numbers as above!
Let us say we have a marketing plan in place to increase the conversion rate for second purchases by 10%. This increase is called “lift”. The final RPP from first to second purchase would end up being around 22% instead of 20%, as shown in the chart above. With this in mind, we can calculate the increased number of customers that would become repeated buyers.
If we had 75,000 customers who made one purchase, we could calculate the number of increased orders with our 20% RPP in the following calculation:
From there, we can distribute the repeated buyers based on our probability distribution and multiply it by AOV, meaning some customers will only buy a second time while others make purchases more frequently.
Based on the information given above, we can sum the total revenue to get a total of approximately $122,000. This number gives us the value of the lift we calculated earlier. With these numbers, we can get even more granular and determine the additional revenue per customer:
This can give you a good idea of how much you can afford to spend when trying to incentivize each customer to buy again. Additionally, if we calculate a similar lift for second time buyers to buy a third time, the value of each additional customer increases to $170 due to the higher probability of each customer to buy more frequently - in other words, to become a loyal customer.
So now when you’re looking to to reach different customer segments, you’ll have all the relevant data in hand for setting the right cost per acquisition (CPA) targets to get your customers to purchase again… and again. The best part of this approach is that customers who have already bought something from you have a much better chance of purchasing again - even first time buyers! This is because they are already familiar with your offering, unlike a cold lead discovering your brand for the first time.
Of course you have to remember that these are just estimates. Both purchase frequency and repeat purchase probability always depend on the nature of your customers, business, products. Use the calculations outlined above as a template for assessing your own stats before taking these examples as law.
You’ll also want to come back to these calculations on an annual basis to track how customer buying behavior changes over time. This will ensure that your acquisition and retention strategies stay aligned with your customer’s behaviors.
How Can You Increase Your Purchase Frequency?
There are a number of different strategies you can use to increase your store’s purchase frequency.
Email marketing is one key way that many ecommerce businesses increase their purchase frequency. When it comes to successful email marketing the key is personalization: send alerts and offers that are relevant to the recipient and related to their previous purchases.
If you run a clothing store and a customer has ordered jewelry on several occasions in the past, you could send them a promotional offer for 10% off any necklace on the site to encourage them to return again. Not only will they be more likely to click on a personalized offer that appeals to their interests, but they’ll also be more likely to choose you over your competitors for their next purchase. The discount included in these personalized messages is simply the cherry on top.
Reward programs can also increase your purchase frequency by establishing a deeper relationship with your customers. Offering them various reward incentives like coupons and freebies encourages shoppers to make repeat orders.
Most reward programs work in such a way that they reward consumers for various valuable actions. These can include completing an order, making referrals, and social sharing. While the specifics are up to you and the scope of your brand, the general idea is that these personalized perks can and will turn your one-time customers into lifelong buyers.
Another great way of increasing your purchase frequency is integrating remarketing into your ad strategy. Have you ever navigated away from an e-store only to see an ad for it on the next site you visit? Then you’ve seen remarketing in action! Tools like Google AdWords and Facebook use remarketing features that allow you to target customers who have visited your website before by showing them relevant ads across devices as they browse the Internet. Once they’re reminded of your products, they’ll come back and complete a purchase, no matter whether it’s their first or their fiftieth.
How Does Knowing Your Purchase Frequency Impact Your Retention Rates?
At the root of any retention strategy is the goal of bringing customers back to your store to buy again. Whether they’ve never bought from you before or they’ve spent thousands on your products, personalized retention marketing will allow you bring every single customer back for that next purchase. Knowing your purchase frequency allows you to take that data and structure personalized marketing efforts around your customers’ demonstrated behavior, as I touched on earlier.
Whether they involve sponsored ads or emails, personalized campaigns have been proven to have a positive influence on retention rates. In fact, over half of online shoppers are more likely to return to a site that utilizes personalization. As experts say, first measure and then improve. You can’t personalize your marketing and sales plan without knowing and understanding your customer data! In that light, purchase frequency is one of the most crucial pieces of data you can calculate.
It’s also important to note that the more often a customer buys, the more likely they are to become a loyal patron. When you look at the profitability of repeat customers, you can see that most customers are only 27% likely to come back again after one purchase. However, that number nearly doubles to 45% once they make a second purchase and shoots to more than 50% after the third.
This tells us that a customer’s potential lifetime value can skyrocket the more often they purchase. Even better: turning a one-time buyer into a repeat customer can increase the chance of securing a fan for life. Integrating purchase frequency data into your marketing efforts can make this dream a reality.
This guest post was written by Evaldas Miliauskas, co-founder of StackTome.
For more information, contact him at firstname.lastname@example.org.